Multiresolution-histogram indexing and relevance feedback learning for image retrieval

Two fundamental aspects for content-based image retrieval system are studied: visual feature extraction and retrieval system design. (i) Feature extraction shares some common properties with image compression where the multiresolution nature of wavelet decomposition can be exploited. When wavelet coefficients are vector quantized, the information content in each spatial-frequency subband is mapped onto coding labels. Thus, the statistics of these labels will reflect the subband characteristics of an image. This constitutes a feature vector that is then used for image matching. (ii) The proposed retrieval system supports queries based on system-user interaction that utilise a non-Euclidean similarity measure. A non-linear function based on a radial basis function (RBF) is adopted for characterising the behaviour of human users in an interactive section where relevance feedback is applied. Experimental results show that the retrieval efficiency is considerably improved by implementing the proposed approach.